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As climate change urgency intensifies, understanding its impact on destinations and the development of adaptation strategies becomes critical for sustainable tourism. This article, through a meta-analysis of 290 climate change elasticity estimates in tourism from 34 studies, examines the influence of climate factors and tourists' adaptive behaviours on tourism demand. It also projects the situations countries will face by 2050 under ongoing climate change scenarios. Findings suggest that low- and middle-income economies and Small Island Developing States, despite their minimal carbon contributions, will face significant tourism losses. In contrast, high-income countries might see increased travel demand and lead to more emissions. This will aggravate climatic conditions and inflict greater losses on less-developed communities. This finding exemplifies a vicious cycle of climate injustice, highlighting the disparities in climate-related social costs globally. [ABSTRACT FROM AUTHOR]

After the temporary shock of the Covid-19 pandemic, the rapid recovery and resumed growth of the tourism sectors accelerates unsustainable tourism, resulting in local (over-)crowding, environmental damage, increased emissions, and diminished tourism acceptance. Addressing these challenges requires an active visitor management system at points of interest (POI), which requires local and timely POI-specific occupancy predictions to predict and mitigate crowding. Therefore, we present a new approach to measure visitor movement at an open-spaced, and freely accessible POI and evaluate the prediction performance of multiple occupancy and visitor count machine learning prediction models. We analyze multiple case combinations regarding spatial granularity, time granularity, and prediction time horizons. With an analysis of the SHAP values we determine the influence of the most important features on the prediction and extract transferable knowledge for similar regions lacking visitor movement data. The results underline that POI-specific prediction is achievable with a moderate relation for occupancy prediction and a strong relation for visitor count prediction. Across all cases, XGBoost and Random Forest outperform other models, with prediction accuracy increasing as the prediction time horizon shortens. For effective active visitor management, combining multiple models with different spatial aggregations and prediction time horizons provides the best information basis to identify appropriate steering measures. This innovative application of digital technologies facilitates information exchange between destination management organizations and tourists, promoting sustainable destination development and enhancing tourism experience. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of accreditations on tourism demand, namely, World Heritage List (WHL) designations and Geographical Indications (GIs). The analysis examines Italian data at the NUTS3 level spanning from 1999 to 2019. Contribution of the study is two-fold. Firstly, by utilizing a Difference-in-Differences model in conjunction with Propensity Score Matching (PSM-DiD), the study evaluates the impact of jointly recognizing both accreditations as a binary treatment (yes/no). Secondly, by employing Generalised Propensity Score Matching (GPSM) for continuous treatment, the study gauges how the impact of the two accreditations is contingent on the number of recognitions (consistency). These impact assessments concern both domestic and international tourists. Conclusions highlight the positive effects, encompassing tourism arrivals, overnight stays, and tourist expenditures, associated with both the combined recognition (PSM-DiD) and the consistency of accreditation (GPSM). These findings support the implementation of place-based policies promoting diverse local assets, notably cultural heritage and agri-food excellence. [ABSTRACT FROM AUTHOR]

Tourism demand theory postulates that tourists' incomes, prices in destination countries, and prices in tourists' countries of origin are key determinants of tourism demand—but must this hold for long-haul travel? We find the answer to be no. By analyzing three international tourist archetypes arriving in New Zealand, a remote island nation, from seven countries, we demonstrate that neither incomes nor prices affect tourism demand in the short run. In the long run, some evidence suggests that tourism demand is income-sensitive; however, it remains insensitive to price changes. We also show that estimates of price elasticities are sensitive to how models are specified and should be interpreted cautiously. The results of this study are broadly relevant to long-haul destinations worldwide. [ABSTRACT FROM AUTHOR]

Existing research has shown that political crisis events can directly impact the tourism industry. However, the current methods suffer from potential changes of unobserved variables, which poses challenges for a reliable evaluation of the political crisis impacts. This paper proposes a panel counterfactual approach with Internet search index, which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest. It also provides a tool to examine potential channels through which the crisis may affect tourist outflows. This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea. Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place. This paper provides essential evidence to help decision-makers improve the management of the tourism crisis. [ABSTRACT FROM AUTHOR]

The tourism literature has shown that a combination of tourism forecasting models can provide better performance than individual models. In the literature, the models to be combined are usually subjectively selected and authors focus on the combination method. In this study, a new direction is represented by the integration of forecasts from several models using a two-stage forecasting system. In stage I, a subset of the best available models is objectively selected according to their performance. Then, in stage II, linear and nonlinear combination methods are utilized to integrate the forecast values of the optimal subset. The empirical study using tourist arrivals in Hong Kong from five major source markets indicates that the proposed two-stage tourism forecasting system with linear combination substantially improves forecasting performance compared with benchmarks. This study also finds that when combining forecasts from different models, the linear combination method is more suitable than nonlinear AI models. [ABSTRACT FROM AUTHOR]

This paper develops a novel Bayesian heterogeneous panel vector autoregressive model (B-HP-VAR) that quantifies the impact of geopolitical risk shocks on the tourism industry of 14 emerging market and developing economies (EMDE). We find that increasing geopolitical tensions have a persistent negative effect on tourism demand in most of these countries, as shown by our impulse response estimates. Furthermore, evidence from forecast error variance decomposition reveals that geopolitical risk shocks in many EMDE economies constitute the main driver of tourism demand. Analysis from historical decompositions demonstrates that geopolitical tensions have been particularly influential in driving tourism demand in Ukraine, Russia, Turkey, China, Indonesia, Thailand, Colombia, and Mexico. Our main findings are robust to several perturbations to the benchmark specification. Our results have several important implications for policymakers in their efforts to strengthen the ability of the tourism industry to absorb shocks from geopolitical tensions. [ABSTRACT FROM AUTHOR]

Abstract This study, leveraging search engine data, investigates the dynamics of China's domestic tourism markets in response to the August 2022 epidemic outbreak in Xinjiang. It focuses on understanding the reaction mechanisms of tourist-origin markets during destination crises in the post-pandemic phase. Notably, the research identifies a continuous rise in the potential tourism demand from tourist origin cities, despite the challenges posed by the epidemic. Further analysis uncovers a regional disparity in the growth of tourism demand, primarily influenced by the economic stratification of origin markets. Additionally, the study examines key tourism attractions such as Duku Road, highlighting its resilient competitive system, which consists of distinctive tourism experiences, economically robust tourist origins, diverse tourist markets, and spatial pattern stability driven by economic factors in source cities, illustrating an adaptive response to external challenges such as crises. The findings provide new insights into the dynamics of tourism demand, offering a foundation for developing strategies to bolster destination resilience and competitiveness in times of health crises.

The purpose of this study is to use an extended gravity model to examine the factors that influence the demand for tourism from origin nations to Vietnam. Data from eleven major marketplaces were gathered for the study between 2005 and 2020. The research employs stepwise regression techniques to choose variables and testing procedures to identify the most suitable model among Ordinary Least Squares (OLS), Random Effects Model (REM), Fixed Effects Model (FEM), and Feasible Generalized Least Squares (FGLS) models. The gravity model comprises variables associated with geographic features, tourism supply capacity, demand attributes, and interconnected variables. Surprisingly, geographical distance holds no statistical significance in the international tourist demand model based on the gravity model, a phenomenon bolstered by globalization. Conversely, the emerging investment factor in restaurants and hotels emerges as the most pivotal determinant influencing tourism demand. Hence, the tourism sector must devise nation-specific policies that target key influencing factors and actively appeal to potential source markets to attract visitors.

This study, leveraging search engine data, investigates the dynamics of China's domestic tourism markets in response to the August 2022 epidemic outbreak in Xinjiang. It focuses on understanding the reaction mechanisms of tourist-origin markets during destination crises in the post-pandemic phase. Notably, the research identifies a continuous rise in the potential tourism demand from tourist origin cities, despite the challenges posed by the epidemic. Further analysis uncovers a regional disparity in the growth of tourism demand, primarily influenced by the economic stratification of origin markets. Additionally, the study examines key tourism attractions such as Duku Road, highlighting its resilient competitive system, which consists of distinctive tourism experiences, economically robust tourist origins, diverse tourist markets, and spatial pattern stability driven by economic factors in source cities, illustrating an adaptive response to external challenges such as crises. The findings provide new insights into the dynamics of tourism demand, offering a foundation for developing strategies to bolster destination resilience and competitiveness in times of health crises. [ABSTRACT FROM AUTHOR]

The study employs dynamic system GMM estimations to investigate factors influencing tourist travel choices from within Sub-Saharan Africa (SSA). Key determinants include GDP per capita, exchange rates, relative prices, and transportation costs. Globalization, border policies, and regional trade agreements also impact tourism patterns in SSA. Technology development, specifically internet accessibility, plays a pivotal role in destination selection. Surprisingly, results indicate a preference for less urbanized areas among international intra-SSA tourists. The study suggests that motivations for traveling from within SSA are largely similar to those for international tourists. This research provides valuable insights for stakeholders in the tourism industry and lays the foundation for further exploration of this dynamic sector in the region. [ABSTRACT FROM AUTHOR]

This paper explores the mitigating effect of democratic institutions in the nexus of tourism and pandemic‐induced uncertainty in 24 Organisation for Economic Co‐operation and Development (OECD) countries. Utilizing dynamic panel data analysis in the modeling process, the findings reveal that increased pandemic‐related uncertainty in the target market negatively affects the tourism demand. However, the presence of democratic institutions in the destination country positively affects the tourism demand. Finally, democratic institutions help mitigate the negative effects of pandemic uncertainty on tourism demand. The research highlights that a democratic destination can provide a more favorable environment for tourism development amid pandemic uncertainty. [ABSTRACT FROM AUTHOR]

This study qualitatively and quantitatively analyzes the effect of the digital economy on tourism innovation in China. It discusses the concept of tourism innovation and constructs a variable to measure it. Then, it empirically tests the impact of digital economy on tourism innovation. Econometric results demonstrate that the digital economy has a consistent and significant impact on tourism innovation, highlighting its role in shaping tourism dynamics. It conducts further tests across different stages and addresses endogeneity concerns. Finally, it presents several policy suggestions. [ABSTRACT FROM AUTHOR]

This study examines the effect of resource endowment and corruption on countries' inbound foreign tourism. Specifically, we examine whether growing resource endowment crowds out tourism industry and role of corruption in this process. For this purpose, we utilize panel data of 140 countries from 1995 through 2018. We utilize estimation technique in analysis that takes care of possible endogeneity in the models. Our results show that resource endowment and corruption lead to a reduction in tourism demand. However, the prevailing high level of corruption with resource richness may provide life-blood to the tourism sector. On the basis of these results, we argue that the 'Dutch disease' hypothesis for tourism is valid for the tourism sector across the world. [ABSTRACT FROM AUTHOR]

The extant tourism literature has identified a range of economic, geographic, political, and social factors impacting tourism demand. However, existing research has ignored how financial inclusion influences tourism demand, especially in the context of emerging markets and developing economies (EMDEs). We make an empirical contribution in this paper by being the first to distinctly capture the impact of financial inclusion on tourism demand for 85 EMDEs across the world, spanning the time-period of 1995–2017. Using various measures of financial inclusion, we find robust evidence to support our conjecture that higher financial inclusion strongly augments higher tourism demand in an unambiguous manner. Our results also show that the impact of financial inclusivity is not non-linear, implying that financial inclusion in its entirety plays a crucial role in supporting countries to generate more tourism revenues regardless of their levels of financial inclusion, which has significant policy implications. [ABSTRACT FROM AUTHOR]

Due to advancements in information technology and growing eco-tourism demand, National Park Network Attention (NPNA) has emerged as a novel indicator of tourism appeal and ecological value recognition. Utilizing Baidu search index (accessed in 2023) data from 2013 to 2022, this study employs time series analysis, index analysis, and spatial statistics to measure and differentiate the spatial and temporal aspects of NPNA across 31 provinces, regions, and municipalities in mainland China, while systematically assessing the impact of various factors from both source and destination perspectives. Over the period of 2013 to 2022, NPNA has increased annually, peaking around holidays and during spring and autumn, demonstrating pronounced seasonality and precursor effects, while exhibiting volatility due to external events. Influenced by factors from both source and destination perspectives, the spatial distribution of NPNA displays a trend of being "high in the east and low in the west" and "high in the south and low in the north", though regional disparities are diminishing. The population size in the source areas remains the dominant factor influencing NPNA, while the concept of national parks is not yet widely recognized. The destination's tourism resource endowment, media publicity, accessibility, and level of informatization are significant influences. An effective integration of resources and marketing is essential for boosting NPNA. The findings provide valuable insights for optimizing the spatial layout of national parks, enhancing the tourism service system, innovating communication and promotional strategies, and improving national park governance effectiveness. [ABSTRACT FROM AUTHOR]

Tourists' images of destinations posted on online travel agency (OTA) platforms are important information sources for potential tourists to perceive and construct destination images. These perceptions can then inform travel decisions. We investigated the roles of the aesthetics of user-generated images on OTA platforms in tourism demand forecasting. Specifically, the aesthetics of images of three popular scenic spots in Hong Kong were used to predict tourism demand from the region's two largest short-haul markets and largest long-haul market using a seasonal autoregressive moving average with exogenous factors (SARIMAX) model. Seasonal naïve, SARIMA, and SARIMAX models involving search query data were taken as benchmarks. Results showed that (1) image aesthetics could help make more accurate tourism demand forecasting; and (2) as an additional variable, image aesthetics could supplement search query-based volume variables to enhance tourism demand forecasting. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to analyze the factors that may stimulate international tourism demand for Turkey. In empirical analyses, the fuzzy logic approach to gravity model with data from 83 countries that sent tourists to Turkey in 2019 are used. GDP per capita (GDPpp), population (POP), distance (DIST), inflation (INF), exchange rate (EXCR), tourism expenditure (EXPD) and tourist departure (DEP) variables are used in the analysis. As a result of the study, only population, distance, expenditure and departure variables were found to be significant and only population variable was found to be fuzzy. According to this finding, the effect of the population variable on the number of tourists appears to be positive in all cases. However, the unit effect of change in population is not clear-cut and can be expressed by values varying within a range. [ABSTRACT FROM AUTHOR]

This research aims to quantify the effect of air transport capacity on tourism demand by examining their long-term (cointegrating) relationship, accounting for cross-sectional dependence and endogeneity. Panel time series data from 2008Q1 to 2019Q4 for international tourist arrivals from 16 main origins to six Australian states are investigated. The study finds 1% increase in Available-Seat-Kilometers, seat capacity, or flight frequency can result in 0.4%–0.7% increase in tourist arrivals to Australia, adding to the body of evidence that shows a non-negligible aviation-led generative effect on tourism demand. The study finds ignoring cross-sectional dependence can result in significantly different, and potentially incorrect, coefficient estimates. Although using pre-COVID data, the results are useful in highlighting the likely aviation supply—tourism demand relations under reasonably well-performing market conditions. For greater tourism demand, findings call for more liberal international air services agreements, and direct/indirect air route development subsidies with minimum commitment of several years. [ABSTRACT FROM AUTHOR]

Although the tourism sector has witnessed a gradual recovery during the last years from the devastating impact of the COVID-19 pandemic, it is still too soon to discuss about a full recovery, as territories have responded and adapted in diverse manners to the shock. As such, it is important to investigate the impact of the pandemic in different moments and at different geographical scales, in order to identify and understand the contrasts between territories of same countries. In light of these facts, the purpose of the current paper is to assess the evolution and the spatial patterns of tourism demand in Romania under the impact of the COVID-19 pandemic for the period of time 2020-2023. The focus is on identifying the differences between regions and destinations in terms of initial impact of the pandemic and of their path to recovery to pre-pandemic levels of tourist arrivals.

This paper shows the fluctuations of tourist demand for the destination Romania over the last three decades (1990-2022) and explains their complex economic, social and political causes. Tourism was probably the economic sector most affected by the COVID-19 pandemic. However, tourism has quickly picked up after lifting the restrictions of the pandemic period. The significant increase of tourism demand in recent times is the effect of implementing coherent action plans, coordinated nationally and supported by certain government measures to encourage the tourism industry, such as the investments in tourism infrastructure and in Romanian destinations’ marketing and promotion. Tourism will contribute significantly to Romania’s economic growth and development. The focus will be on the quality and diversity of tourist offer for the incoming visitors. Given the current conditions, generated by the pandemic and the war in Ukraine, a doubling of foreign tourist arrivals and an increase in the average length of their stay are expected for the next 10 years.

Tourism is one of the important activities of human life which helps to overcome an individual from hectic schedule or boredom and gives leisure and pleasure. Tourism demand is better known as Travel Purchase Decision depends on various factors like Perceived Risk, Personal Factors, and Knowledge of the destination. Many pieces of research based on these factors have been done earlier but not in the tourism sector. It is one of the prime reasons to conduct this research in the Gwalior region. This study examined the impact of Perceived Risk, Personal Factor and Knowledge of Destination on Travel Purchase. The 5 points Likert scale-based questionnaire was filled with online and offline users in the Gwalior region and applied tests like Reliability and the Univariate ANCOVA. By the study, it has been found that the Perceived Risk and Knowledge of Destination have a significant impact on Travel Purchase Decision whereas Personal Factors have no significant impact. [ABSTRACT FROM AUTHOR]

The worldwide COVID-19 pandemic has affected the tourism sector by closing borders, reducing both the transportation of tourists and tourist demand. Due to the country-wide lockdown, most activities in the hotel, motel, restaurant, and transportation sectors have been postponed. Consequently, the article investigates four research issues by examining the consequences of global tourism in the private sector before and after COVID-19. As an analytical method, the article suggested qualitative research methodologies to collect information from tourism employees. The opinions of the respondents were gathered through online emails in the questionnaire survey. Further, the article considers people's future desire for specific tourism destinations based on visitor arrivals. Forecasting tourist demand is an essential component of good and efficient tourism management. Consequently, the article proposes an attention-based long short-term memory model for exact demand forecasting. The experimental findings reveal that the model's minimal prediction error accuracy is 0.45%, which indicates that it has a more robust prediction effect, a faster convergence rate, and a greater prediction accuracy. Seasonality has emerged as one of the most distinguishing and defining characteristics of the global tourist business. Accordingly, the article mandated to compare the seasonal and non-seasonal effects of the tourist sector throughout the years 2020–2021. Moreover, Governments must analyse the crises' long-term consequences and, as a result, define the components that constitute government advantages supplied to the tourist sector during the pandemic era. As a result, many governmental policies, especially those about social welfare, may perceive a fresh start during the post-pandemic period, respectively. [ABSTRACT FROM AUTHOR]

Objective: The dynamics of domestic tourism have gained prominence, especially in the wake of global disruptions such as the COVID-19 pandemic, which shifted travel preferences towards more localized experiences. This study aims to identify and analyze the dimensions of domestic tourism demand in Tehran, focusing on the motivations, barriers, preferences, and impacts of tourism within the city. The objective is to provide a comprehensive understanding that can inform policy and strategic planning to enhance Tehran's appeal as a domestic tourist destination. Methodology: A qualitative research approach was employed, utilizing semistructured interviews as the primary data collection method. The study targeted a purposive sample of local residents, domestic tourists, and professionals within the tourism sector in Tehran. Theoretical saturation guided the data collection process, ensuring a thorough exploration of themes. Data were analyzed through thematic analysis, allowing for the identification of key patterns, themes, and categories within the data. Findings: The study unveiled five main themes: Motivations for Domestic Tourism, Barriers to Domestic Tourism, Perceptions of Tehran as a Tourist Destination, Tourist Preferences and Behaviors, and the Impact of Domestic Tourism. Each theme comprised various categories with associated concepts, ranging from cultural interest, relaxation, and social connections to economic factors, safety concerns, and the broader economic, cultural, and social impacts of tourism. These findings underscore the complex interplay of factors influencing domestic tourism demand in Tehran. Conclusion: This research highlights the multifaceted nature of domestic tourism demand in Tehran, revealing a spectrum of motivations driving tourists, alongside significant barriers that need addressing. The study underscores the potential of domestic tourism as a catalyst for economic development, cultural preservation, and social cohesion in Tehran. By addressing identified barriers and leveraging the motivations and preferences of domestic tourists, Tehran can enhance its domestic tourism sector. [ABSTRACT FROM AUTHOR]

The present study investigates the impacts of exchange rate and exchange rate volatility on inbound tourism demand in Mauritius, using annual data covering the period 1999–2019 and a panel autoregressive distributed lag model. In particular, we consider 17 major tourism markets for Mauritius. The sample is further divided into Eurozone (7 countries) and non‐Eurozone (10 countries) for additional insights. The results show that in the long run, both the exchange rate and its volatility have negative and significant impacts on tourism demand across the three samples considered, while income in the home country has a positive and significant effect on tourism demand. On the other hand, relative price is found to have a significant and negative effect on tourism demand only for the overall sample of 17 countries, while for both the Eurozone and non‐Eurozone, the impact is insignificant. The empirical results suggest that policymakers should give consideration to stronger monetary and fiscal regulations to increase tourism demand. [ABSTRACT FROM AUTHOR]

We estimate the impact of political instability and the population of immigrants in Australia on the flow of international tourist arrivals. We hypothesize that political instability has a short-term negative effect while the population of immigrants in Australia may have a positive or negative effect depending on time span. Moreover, we postulate that the population of immigrants resorbs in the short run partially or totally the adverse effect of political instability. Our empirical strategy takes into consideration potential heterogeneity among cross-sections and differentiates between short-term and long-term effects. For this purpose, we use the Pooled Mean Group estimator in a panel Autoregressive Distributed Lag model. Findings from the pooled estimations suggest that, in the short run, the population of immigrants in Australia reduces the negative effect of political instability on international tourism flows. However, its effect is negative in the long run. We also find evidence for heterogeneity across countries. [ABSTRACT FROM AUTHOR]

Structural breaks represent periods of turmoil that may influence how tourism affects economic growth. Current research on the tourism-led growth hypothesis (TLGH) measures the effect of structural breaks using dummy variables in regression models. However, the drawback of this approach is that there could be multiple structural breaks which result in an overfitting problem and reduce degrees of freedom in small samples. It also becomes difficult to isolate the effect of individual breaks when multiple structural breaks occur within the same year. We thus highlight the role of the Fourier ARDL model in addressing these shortcomings. We use three Pacific Island Countries: Fiji, Tonga, and Vanuatu as case studies to evaluate the efficacy of the Fourier ARDL model. Contrary to earlier research, our results indicate that tourism does not always lead to economic growth. Appropriate modelling of structural breaks also influences the outcome of asymmetric effects. These findings imply that future research should pay close attention to the effects of structural breaks in the TLGH. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 outbreak, forecasting the tourism demand of tourist attractions is facing unprecedented difficulties given the lack of understanding about the pandemic impacts and the unavailability of post-pandemic data for generating forecasts. In this study, two strategies are proposed to improve forecasting performance and address the above difficulties. First, a novel COVID-19 impact indicator is built to reflect the impacts of the pandemic on tourism demand. Second, an effective forecast aggregation algorithm is developed to efficiently generate forecasts despite limited post-pandemic data availability. To validate the effectiveness of these strategies, an empirical study using real data from a tourist attraction is conducted, and results demonstrate that these strategies improve the overall forecast performance, including forecast accuracy and stability. [ABSTRACT FROM AUTHOR]

This study examines the impact of income, exchange rates, political conditions, and health factors on Indonesian tourism demand from 2010 to 2019, using multiple and panel regression analyses with data from Statistics Indonesia (BPS), the World Bank, the Central Bank of Indonesia (BI), and the World Health Organization (WHO). The findings indicate that the income of tourists from China, Australia, Timor Leste, and other analyzed countries positively affects tourism demand, while income from Malaysia and Singapore shows no significant effect. Exchange rates positively influence tourists from Malaysia, Singapore, Australia, Timor Leste, and other analyzed countries, but have no significant effect on Chinese tourists. Political conditions negatively impact Australian tourists, positively influence tourists from other analyzed countries, and have no significant effect on tourists from Malaysia, China, Singapore, and Timor Leste. Health factors negatively affect tourists, while having no significant effect on tourists from Singapore and Australia. The study recommends that the government enhance tourism supply to boost demand, with a focus on improving environmental sustainability, health, tourism service infrastructure, and security and safety. Additionally, there is a need for standardized governance rules to better manage the impacts of outbreaks or disasters on the tourism sector.

This study examines whether price and income elasticities in international tourism demand can be asymmetric depending on the direction of change. We consider Fiji as a reference destination with the following source markets: Australia, Canada, Japan, New Zealand, United Kingdom, and the USA. A sample from 1978 to 2018 is considered. Symmetric price and income elasticities are found for Australia and New Zealand. Asymmetric price and symmetric income elasticities are found for Canada and Japan. Price and income asymmetries are found for the UK and USA. The implication for research on tourism demand is that linear models may be miss-specified and that better predictions of tourism demand may result from the nonlinear models. [ABSTRACT FROM AUTHOR]

With the emergence of the metaverse, countries' digital efforts to create tourism opportunities have given rise to the possibility of capitalising on digital content which, along with physical tourism experiences, can generate further income and enhance a country's reputation. Non-fungible tokens (NFTs), a unique application of blockchain technology, offer an enabling technology in several sectors, including tourism. Therefore, this study aims to explore the official tourism websites of Croatia and Slovenia and analyse current NFT applications in tourism economics. The methodology focuses explicitly on sentiment analysis, blockchain and machine learning. The paper introduces various applications currently in place, including Slovenia's "I Feel Nft" project. The research shows that the main benefits of using NFT and sentiment analysis in the tourism economy are the promotion and presentation of major tourist destinations, exhibitions, works of art, and companies' products in tokens, digital content and souvenirs. The adoption of sentiment analysis and NFTs in the tourism economy is still open to proposals for implementing public quantitative data metrics. Therefore, the scientific contribution of this research is essential in terms of operational recommendations and defining metrics for measuring the effectiveness of those methodologies and their applications in the tourism economy. On top of that, the practical contribution lies in monitoring the influx of tourists, and highlighting their increase over time and the significance of new technology in time series tourism research. [ABSTRACT FROM AUTHOR]

Although the tourism sector has witnessed a gradual recovery during the last years from the devastating impact of the COVID-19 pandemic, it is still too soon to discuss about a full recovery, as territories have responded and adapted in diverse manners to the shock. As such, it is important to investigate the impact of the pandemic in different moments and at different geographical scales, in order to identify and understand the contrasts between territories of same countries. In light of these facts, the purpose of the current paper is to assess the evolution and the spatial patterns of tourism demand in Romania under the impact of the COVID-19 pandemic for the period of time 2020-2023. The focus is on identifying the differences between regions and destinations in terms of initial impact of the pandemic and of their path to recovery to pre-pandemic levels of tourist arrivals. [ABSTRACT FROM AUTHOR]

This paper aims to understand better how attitudes towards the environment could influence preferences and willingness to pay for the development of sustainable tourism products on the Spanish island of Gran Canaria. A hybrid choice model is estimated to analyse how different latent constructs related to environmental concerns affect individuals' preferences for a set of sustainable tourism activities. The data used in the analysis are obtained from a discrete choice experiment where different scenarios with nature-based tourism packages are created. A set of measurement indicators allowed us to gain insight into the underlying latent structure regarding the individuals' attitudes towards the environment. The analysis consists of integrating these attitudes into a choice model, focusing on a market segment primarily composed of potential customers who are young residents and non-residents. The results reveal significant heterogeneity in preferences and willingness to pay for the various activities under study when attitudinal latent factors are incorporated into the model. Our findings provide valuable insights for researchers, policymakers, and practitioners promoting sustainable tourism products. [ABSTRACT FROM AUTHOR]

The coronavirus epidemic (COVID19) has affected the global economy and the services sector. Quarantine measures related to travel restrictions have led to an unprecedented decline in the tourism industry with repercussions on tourism service providers, transport companies and state budgets. Travel is necessary for tourism, therefore, any factor that prevents travel can have a profound impact on the tourism industry. In the current pandemic context, the forecast in the field of tourist travel has played an important role in supporting the revival of this sector. In this study, econometric and interpretive methods were combined to predict the demand. In this study we approached a prediction model that is based on the seasonal stationary and adjustment of observed and FFT data. Experimental results show that the proposed prediction model has demonstrated a good medium-term forecast and can be used successfully in short and medium periods of time. For a certification of the exploratory evaluation of tourism forecasts there were comparatively analyzed the results obtained for three countries in south-eastern Central Europe, countries with similar natural and anthropic tourist resources (Bulgaria, Croatia and Romania). [ABSTRACT FROM AUTHOR]

This study investigates the influence of price factors on tourist visits to Malaysia from 21 countries. The study samples chosen in this study are between 2000 and 2019, before the COVID-19 outbreak. Panel approaches are utilized on five regions. The results show that the tourism demand from Asian countries has a positive impact on tour prices and income, but these variables negatively affect the tourism demand from ASEAN and western countries. Most tourists in the regions choose Singapore and Indonesia as substitute destinations, while Thailand is a complementary destination for Malaysia's tourism industry. The findings are invariant among the regions. However, traveling costs do not reduce the tourism demand; hence, this factor is negligible for Malaysia. In addition, the tourism demand from ASEAN countries increases with a depreciation in Ringgit Malaysia, but the effect is the opposite for China, Asia, and western countries. The overall findings show that different regions react differently to price factors. [ABSTRACT FROM AUTHOR]

Context and Purpose: Understanding the relationship between financial development and tourism activities is crucial to attracting investment and promoting sustainable growth. Tourism expenditures and international tourist arrivals as components of tourism demand affect economic growth directly or through their influence on financial development. Design/methodology/approach: Using the Autoregressive Distributed Lag Model (ARDL) for the data on Iran's economy from 1995 to 2018, this study examined the relationship between tourism demand and the level of financial development. Then, it investigated the impact of financial development on the relationship between tourism demand and GDP per capita growth. Findings: The results indicated a significant positive relationship between tourism demand and financial development; for the one percent growth of tourism expenditures, the financial development increases by 0.3423 percent. Moreover, a one percent rise in the arrival of international tourists leads to a 0.609 percent increase in financial development. The interactive effect of financial development and tourism expenditures on the growth of per capita production is 0.4264, which is statistically significant. Therefore, it can be argued that enhancements in financial development strengthen the effect of tourism expenditure on GDP per capita growth. Conclusion: The results are consistent with the tourism-led growth hypothesis and suggest authorities improve economic growth by strengthening the tourism sector and financial development. Originality/value: Paying attention to the impact of tourism demand indicators on financial development, besides their interactive effect on the growth of per capita production, covers the existing study gap and is considered the advantage of the present study. [ABSTRACT FROM AUTHOR]

Kukup Beach is one of the beaches in Gunung Kidul, Yogyakarta whose management is still in the developing stage. However, the demand for marine tourism is quite busy with the lack of studies and research on the potential and analysis of management recommendations for Kukup Beach. The aim of the research is to analyze tourism demand for managing marine ecotourism at Kukup Beach. The research was conducted in May 2023 at Kukup Beach using a structured questionnaire data collection method and the number of respondents was 103 people. Data analysis uses multiple linear regression statistics and descriptive analysis of ecotourism development. The results obtained are that the characteristics of Kukup beach tourists form a pattern with dominant tourists of productive age (18-30 years), female with a relatively high level of education. Tourist visits are influenced by travel cost, gender, income, education, age, and distance. The tourism demand function is obtained as follows y = 6.223-5.20-07c-0.767g+1.31-06i-0.508e-0.060a-0.783d. Management of Kukup Beach tourism using Ecotourism principles pays attention to visit management considering the characteristics of young tourists, it applies travel rules in accordance with conservation principles. In this way, Kukup Beach ecotourism management can develop and be sustainable. [ABSTRACT FROM AUTHOR]

Migration and migration-caused concerns and uncertainties have been rising dramatically in recent years at the global level. However, our knowledge of the impact of migration-related concerns on tourism is limited. To address this gap, we estimate the impact of the US migration fears on tourist arrivals from Western Europe to the US using a tourism demand model. The paper employs a new robust cointegration approach with instrumental variables. Our estimates suggest that tourists from Western Europe have a negative and significant long-term response to the fear of migration in the US. The results show that migration-related concerns are constraints on the development of the tourism industry in the US. [ABSTRACT FROM AUTHOR]

The present empirical study aims to determine the key factors influencing the travel intention of persons with disabilities who live across the Tungurahua Province, Ecuador. A cross-sectional survey was conducted between September 2019 and mid-March 2020, and collected 473 valid questionnaires containing critical components of the medical and social models of disability. Collected data were quantitatively analysed with multiple regression analysis. Results showed that some crucial elements of the social model of disability (i.e., socio-demographic features and destination quality dimensions) and an element of the medical model of disability (i.e., dependency or need of other persons) significantly affect the travel intent ions of persons with disabilities (p

The tourism market is the economic space in which all transactions in the form of tourism consumption and production take place. Although competition is the main instrument that defines a free market economy, nevertheless in the tourism market it is necessary for the state to intervene by establishing certain regulations concerning the provision of tourism services by hoteliers and tourism intermediaries in order to ensure the protection of tourists as consumers of tourism services. The tourist market is a complex and dynamic market, which is constantly subject to both endogenous and exogenous factors, such as economic, political, social, legislative, psychological, natural, geographical, military and health factors. In Romania, the tourism market has shown trends of decline, stagnation or growth under the influence of factors specific to the Romanian economy, the aim of this paper being to highlight these aspects.